Data Integrity: What it Means and Why Any Organization Should Maintain It

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Understand what data integrity is, how it differs from data quality, and learn how to preserve it in your company.

Data integrity is the backbone of all sound business decisions. Why? Let’s give it some thought.

Indisputably, we operate in a data-driven world, where data is the cornerstone of today’s economy and business environment. Organizations are constantly dependent on data in relation to their operations, clients, financial operations, and so on. This growing trend has been led by the exponential growth of technology. Nonetheless, as the volume of data we receive and use has risen incredibly fast, the integrity of data is not taken into consideration all too frequently.

It has become increasingly difficult to preserve data integrity while it is exchanged by various entities. Although the benefit of distributing information is obvious, businesses sometimes struggle with low-quality data. But one way to minimize downtime, improve processes, and better use data for business decision-making can be done by addressing data integrity issues.

To explain what data integrity entails and how to deal with the issues it may pose, I’ve written this comprehensive guide for anyone in search of actionable advice and best practices on how to maintain data integrity.

In this article, I will explore topics such as:

• What data integrity is and why it is important
  
• Types of data integrity
  
• How data integrity differs from data quality
  
• Attacks on data integrity
  
• How to preserve data integrity in your organization
  

So, by the end of this piece, you will know what data integrity is, why it matters, and what you can do to uphold your data integrity.

Data integrity definition

In today’s information age, it is absolutely critical to enforce policies that protect the quality of gathered data, since more pieces of information are analyzed and stored now than ever.

The first move towards maintaining your data safe is to learn the basics of data integrity.

What is data integrity?

According to Techopedia:
 

“Data integrity is the overall completeness, accuracy, and consistency of data. This can be indicated by the absence of alteration between two instances or between two updates of a data record, meaning data is intact and unchanged.”

By the usage of standard protocols and guidelines, data integrity is typically imposed during the design and creation process of a data repository. It is preserved by the use of different methods and validation protocols for error-checking.

Data integrity is assessed by its authenticity, completeness, and transparency. In addition, the integrity of data also requires ensuring that organizations comply with the regulations in place and the identification of security lapses. By enforcing a series of protocols, instructions, and criteria, this status is attained.

Fundamentally, data integrity is maintained by designing a framework where data cannot be tinkered with or manipulated.

Database data integrity

Data integrity is, in a wide context, a concept for recognizing the integrity and preservation of all data.

However, the term is also connected with database management.

Whenever data is managed and processed, there is a possibility that it might get damaged any time data is processed – maliciously or inadvertently. Preserving the integrity of data helps ensure that the information stays unchanged and unaltered during its entire lifespan. For instance, a user might mistakenly insert a phone number into a date section. Should the database uphold data integrity, it would prevent these errors from happening.

Maintaining data integrity should become a priority when building databases. For this reason, whenever feasible, a proper database will impose data integrity.

In relation to databases, there are four data integrity categories:

1. Entity integrity
   
2. Referential integrity
   
3. Domain integrity
   
4. User-defined integrity
   

In the following lines, I will describe each type of database data integrity.

#1. Entity Integrity

The integrity of the data ensures each row inside a table is unique (two rows can never be identical). A primary key value can be established to accomplish this. There will be a unique identifier in the primary key field, and two rows will not have the same unique identifier.

#2. Referential Integrity

Referential integrity is associated with relationships, which suggests that we have to guarantee that the foreign key value matches the primary key value at all times when two or more tables have a relationship. Coming across a scenario where, in the primary table, a foreign key value has no matching primary key value is to be avoided, as this will lead to the record becoming orphaned.

Referential integrity will prohibit users from attaching records to a related table if the primary table does not have an associated record, changing values that result in orphaned records in a related table in the primary table, or erasing records from a primary table if similar records are matched.

#3. Domain Integrity

The integrity of the domain involves the authenticity of entries for a certain column. The very first step in preserving domain integrity is choosing the suitable data type for a column. Additional actions could include creating relevant restrictions and rules to determine the format of the data and/or limiting the number of potential values.

#4. User-Defined Integrity

User-defined integrity enables the user to apply rules which are not protected by either of the three forms of data integrity to the database.

Data integrity vs data quality

Data can be the most important resource for a company – but only if it’s data you can actually rely on.

Inaccurate insights, biased observations, and ill-advised suggestions may be the outcomes of unreliable data.

As I previously mentioned, data integrity is a basic feature of information security and relates to the quality and durability of data contained in a database, data center, etc. The concept of data integrity may be used to define a state, a procedure, or a feature and is sometimes used interchangeably with “data quality”.

However, “data integrity” and “data quality are two different terms.

To make informed decisions, any business trying to improve the quality, consistency, and validity of its data needs to grasp the difference between data integrity and data quality.
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